Systems and methods for recommending content

ABSTRACT

Systems, methods, and non-transitory computer-readable media can determine at least one scenario that applies to a user of a social networking system based at least in part on features associated with the user. One or more groups of content recommendations associated with the at least one scenario can be determined. Each group of content recommendations can include a set of content items that relate to the at least one scenario. The one or more groups of content recommendations can be provided to the user as recommendations.

FIELD OF THE INVENTION

The present technology relates to the field of networked communications. More particularly, the present technology relates to techniques for recommending content to users in a computerized networking system.

BACKGROUND

Today, people often utilize computing devices (or systems) for a wide variety of purposes. For example, users can use their computing devices to interact with other users, create content, share content, and view content. In some cases, users can utilize their computing devices to access a social network and post content to the social network. Content posted to the social network may include text content items and media content items, such as audio, images, and videos. The posted content may be published to the social network for consumption by others.

SUMMARY

Various embodiments of the present technology can include systems, methods, and non-transitory computer readable media configured to determine at least one scenario that applies to a user of a social networking system based at least in part on features associated with the user. One or more groups of content recommendations associated with the at least one scenario can be determined. Each group of content recommendations can include a set of content items that relate to the at least one scenario. The one or more groups of content recommendations can be provided to the user as recommendations.

In an embodiment, determining the at least one scenario that applies to the user further includes determining the features associated with the user; providing information describing the features to a machine learning model; and obtaining information indicating the at least one scenario applies to the user from the machine learning model.

In an embodiment, the features associated with the user include at least one of: age, gender, education level, marital status, number of children, location of residence, preferred language, topics of interest, and temporal information.

In an embodiment, content items included in a group of content recommendations all correspond to a same type of content item.

In an embodiment, the content items included in the group of content recommendations are pages, social groups, events, offers, jobs, photos, videos, or posts that are accessible through the social networking system.

In an embodiment, determining the one or more groups of content recommendations associated with the at least one scenario further includes determining a set of content items of a same type and determining a plurality of content items that relate to the at least one scenario from the set of content items.

In an embodiment, content items are determined to relate to the at least one scenario based at least in part on respective topics associated with the content items and subject matter reflected in the content items.

In an embodiment, the systems, methods, and non-transitory computer readable media are configured to obtain a ranking order for the groups of content recommendations based at least in part on the features associated with the user.

In an embodiment, the one or more groups of content recommendations are provided in a content feed accessible to the user through the social networking system.

In an embodiment, the one or more groups of content recommendations are provided as notifications sent through the social networking system.

It should be appreciated that many other features, applications, embodiments, and/or variations of the disclosed technology will be apparent from the accompanying drawings and from the following detailed description. Additional and/or alternative implementations of the structures, systems, non-transitory computer readable media, and methods described herein can be employed without departing from the principles of the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example scenario-based recommendation module, according to an embodiment of the present technology.

FIG. 2 illustrates an example scenario determination module, according to an embodiment of the present technology.

FIG. 3 illustrates an example content determination module, according to an embodiment of the present technology.

FIG. 4 illustrates an example diagram, according to an embodiment of the present technology.

FIG. 5 illustrates an example method, according to an embodiment of the present technology.

FIG. 6 illustrates a network diagram of an example system including an example social networking system that can be utilized in various scenarios, according to an embodiment of the present technology.

FIG. 7 illustrates an example of a computer system or computing device that can be utilized in various scenarios, according to an embodiment of the present technology.

The figures depict various embodiments of the disclosed technology for purposes of illustration only, wherein the figures use like reference numerals to identify like elements. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated in the figures can be employed without departing from the principles of the disclosed technology described herein.

DETAILED DESCRIPTION

Recommending Content

Under conventional approaches, users can access various content through a content provider. Typically, a user operating a computing device can interact with the content provider over one or more computer networks. Further, the accessed content can be presented through a display screen by a software application (e.g., a web browser, social networking application, etc.) running on the computing device. Such conventional approaches permit users to access various types of content. Conventional approaches can also recommend various types of content to users. For example, conventional approaches can recommend content to users based on topics that are of interest to them. However, when recommending content, conventional approaches typically do not consider other factors that may influence the types of content that may be of interest to users or when such content should be recommended to users. Accordingly, such conventional approaches may not be effective in addressing these and other problems arising in computer technology.

An improved approach rooted in computer technology overcomes the foregoing and other disadvantages associated with conventional approaches specifically arising in the realm of computer technology. In various embodiments, users can be recommended various types of content based on scenarios that are determined to apply to the users. Each scenario can be associated with various types of content related to the scenario. For example, a scenario can be associated with related pages, social groups, events, offers, jobs, photos, videos, and posts that are accessible through a content provider (e.g., a social networking system). For example, a scenario that applies to new parents may be associated with various types of content that is relevant to new parents. In various embodiments, a machine learning model can be used to predict which scenarios apply to users. For example, the machine learning model can be trained to predict whether a scenario applies to a user based at least in part on features describing the user (e.g., age, gender, education level, topics of interest, location of residence, preferred language, and user interaction data). After determining the scenario applies to the user, various content associated with the scenario can be provided as recommendations to the user. In some embodiments, each scenario can be associated with one or more groups of content recommendations. In some embodiments, each group of content recommendations includes a set of content items of some uniform type (e.g., pages, groups, events, offers, jobs, photos, videos, posts, etc.). In the foregoing example, the scenario for new parents can be associated with a first group of content recommendations corresponding to pages related to parenting. In another example, the scenario may also be associated with a second group of content recommendations corresponding to social groups for new parents to join. In yet another example, the scenario may also be associated with a third group of content recommendations corresponding to offers for products and services related to new parents. In this example, when the scenario is determined to apply to a user, the content provider can provide one or all of the groups of content recommendations to the user. More details relating to the present technology are provided below.

FIG. 1 illustrates an example system 100 including an example scenario-based recommendation module 102, according to an embodiment of the present technology. As shown in the example of FIG. 1, the scenario-based recommendation module 102 can include a scenario determination module 104, a content determination module 106, and a recommendation module 108. In some instances, the example system 100 can include at least one data store 112. The components (e.g., modules, elements, etc.) shown in this figure and all figures herein are exemplary only, and other implementations may include additional, fewer, integrated, or different components. Some components may not be shown so as not to obscure relevant details.

In some embodiments, the scenario-based recommendation module 102 can be implemented, in part or in whole, as software, hardware, or any combination thereof. In general, a module as discussed herein can be associated with software, hardware, or any combination thereof. In some implementations, one or more functions, tasks, and/or operations of modules can be carried out or performed by software routines, software processes, hardware, and/or any combination thereof. In some cases, the scenario-based recommendation module 102 or at least a portion thereof can be implemented using one or more computing devices or systems that include one or more servers, such as network servers or cloud servers. In some instances, the scenario-based recommendation module 102 can, in part or in whole, be implemented within or configured to operate in conjunction with a social networking system (or service), such as the social networking system 630 of FIG. 6. In some instances, the scenario-based recommendation module 102 can be, in part or in whole, implemented within or configured to operate in conjunction or be integrated with a client computing device, such as the user device 610 of FIG. 6. For example, the scenario-based recommendation module 102 can be implemented as or within a dedicated application (e.g., app), a program, or an applet running on a user computing device or client computing system. The application incorporating or implementing instructions for performing some, or all, functionality of the scenario-based recommendation module 102 can be created by a developer. The application can be provided to or maintained in a repository. In some cases, the application can be uploaded or otherwise transmitted over a network (e.g., Internet) to the repository. For example, a computing system (e.g., server) associated with or under control of the developer of the application can provide or transmit the application to the repository. The repository can include, for example, an “app” store in which the application can be maintained for access or download by a user. In response to a command by the user to download the application, the application can be provided or otherwise transmitted over a network from the repository to a computing device associated with the user. For example, a computing system (e.g., server) associated with or under control of an administrator of the repository can cause or permit the application to be transmitted to the computing device of the user so that the user can install and run the application. The developer of the application and the administrator of the repository can be different entities in some cases, but can be the same entity in other cases. It should be understood that many variations are possible.

The scenario-based recommendation module 102 can be configured to communicate and/or operate with the at least one data store 112, as shown in the example system 100. The at least one data store 112 can be configured to store and maintain various types of data. For example, the data store 112 can store information describing various content that has been shared by users of a social networking system. In some embodiments, the data store 112 can store information describing scenarios and groups of content recommendations associated with those scenarios. In some implementations, the at least one data store 112 can store information associated with the social networking system (e.g., the social networking system 630 of FIG. 6). The information associated with the social networking system can include data about users, social connections, social interactions, locations, geo-fenced areas, maps, places, events, pages, groups, posts, communications, content, feeds, account settings, privacy settings, a social graph, and various other types of data. In some implementations, the at least one data store 112 can store information associated with users, such as user identifiers, user information, profile information, user specified settings, content produced or posted by users, and various other types of user data.

The scenario determination module 104 can be configured to determine scenarios that apply to users. In various embodiments, the scenario determination module 104 can determine scenarios that apply to a given user based on various features corresponding to the user. More details regarding the scenario determination module 104 will be provided below with reference to FIG. 2.

The content determination module 106 can be configured to determine scenario-based content to be recommended to users. For example, in some embodiments, scenarios can be associated with one or more groups of content recommendations. Each group of content recommendations can correspond to some type of content that is accessible through a content provider (e.g., the social networking system 630 of FIG. 6), such as pages, groups, events, offers, jobs, photos, videos, posts, etc. The content determination module 106 can rank these groups of content recommendations for potential presentation to users. More details regarding the content determination module 106 will be provided below with reference to FIG. 3.

The recommendation module 108 can provide content determined by the content determination module 106 as recommendations. For example, the content determination module 106 can determine one or more groups of content recommendations associated with a scenario that applies to a given user. In such embodiments, the recommendation module 108 can provide some or all of the groups of content recommendations to the user. In an embodiment, the recommendation module 108 can provide some or all of the groups of content recommendations in a content feed (or news feed) that is accessible to the user through the content provider. In another embodiment, the recommendation module 108 can provide some or all of the groups of content recommendations in one or more notifications provided to the user through the content provider. In yet another embodiment, the recommendation module 108 can provide some or all of the groups of content recommendations in one or more emails sent to the user. Many variations are possible.

FIG. 2 illustrates a scenario determination module 202, according to an embodiment of the present technology. The scenario determination module 202 can determine scenarios that apply to users of a content provider (e.g., the social networking system 630 of FIG. 6). In some embodiments, the scenario determination module 104 of FIG. 1 can be implemented with the scenario determination module 202. As shown in the example of FIG. 2, the scenario determination module 202 can include a feature module 204 and a prediction module 206.

In various embodiments, the scenario determination module 202 can implement a machine learning model that is trained to predict whether scenarios apply to users. For example, in some embodiments, the machine learning model implemented by the scenario determination module 202 can receive a set of features corresponding to a user as input. Upon processing the set of features, the machine learning model can output information indicating whether any scenarios apply to the user. In general, scenarios can be pre-defined and other scenarios can continue to be added through appropriate training of the machine learning model. In some embodiments, the machine learning model can be trained based on a set of training examples. For example, each training example can include a set of features corresponding to a user and supervisory signals indicating whether that user subscribed to one or more scenarios through the content provider or whether any of those scenarios were otherwise determined to be applicable to the user by the content provider.

The feature module 204 can determine features that can be used to determine whether scenarios apply to a user. In various embodiments, the feature module 204 can determine features from profile data provided by the user, such as age, gender, education level, marital status, number of children, location of residence, preferred language, etc. The feature module 204 can also determine topics that are of interest to the user, for example, based on user interaction data. The user interaction data can describe content which the user interacted (or co-interacted) with through the content provider. Such interactions can include likes, comments, and shares of posts; fanning of pages; joining groups; attending or planning to attend events; offers redeemed; and jobs to which the user applied through the content provider, to name some examples. In some embodiments, the feature module 204 can determine temporal features. Such temporal features can also be evaluated by the machine learning model to predict applicable scenarios. For example, the machine learning model can be provided a set of features corresponding to a user and temporal features indicating a timing of when content recommendations are to be provided to the user. In this example, the machine learning model can predict which scenarios apply to the user based on the features corresponding to the user and also the temporal features. In some embodiments, the temporal features can describe a time of day (e.g., morning, afternoon, evening, late evening), day of week, month, and year. Many variations are possible.

The prediction module 206 can predict whether any scenarios apply to the user. For example, the prediction module 206 can provide features determined by the feature module 204 as input to the machine learning model implemented by the scenario determination module 202. The machine learning model can output information indicating whether any scenarios apply to the user. For example, in some embodiments, the machine learning model can output a vector of values with each value corresponding to a scenario. In such embodiments, each value can indicate a likelihood of a scenario being applicable based on the inputted features. The prediction module 206 can evaluate the outputted information to determine which scenarios apply to the user. For example, the prediction module 206 can determine that a “weekend planning” scenario applies to the user based on information outputted by the machine learning model. The weekend planning scenario may be associated with various types of content related to weekend planning. In another example, the prediction module 206 can determine that a “birthday event” scenario applies to the user based on information outputted by the machine learning model. The birthday event scenario may be associated with various types of content related to planning a birthday event. In various embodiments, such scenario-based content can be provided as recommendations to the user, as described below.

FIG. 3 illustrates a content determination module 302, according to an embodiment of the present technology. The content determination module 302 can determine content to be recommended to users based on scenarios that apply to those users. In some embodiments, the content determination module 106 of FIG. 1 can be implemented with the content determination module 302. As shown in the example of FIG. 3, the content determination module 302 can include a grouped content module 304 and a ranking module 306.

The grouped content module 304 can determine groups of content recommendations that can be presented to users. As mentioned, in various embodiments, each scenario can be associated with one or more corresponding groups of content recommendations. For example, in some embodiments, a group of content recommendations for a scenario can be composed of content items that correspond to a particular type of content (e.g., a group of page recommendations, a group of post recommendations, a group of job recommendations, etc.) and that relate to the scenario. In some embodiments, content items are determined to relate to a scenario based on respective topics (or categories) associated with the content items and subject matter represented in the content items (e.g., text, images, etc.). In some embodiments, user co-interaction data can be used to identify content items that relate to a given scenario. For example, a scenario for users who recently relocated to a new geographic region can be associated with a first group of page recommendations including pages related to the geographic region. In another example, the scenario may also be associated with a second group of social group recommendations including social groups for new transplants to join. In yet another example, the scenario may also be associated with a third group of offer recommendations including offers for local businesses (e.g., restaurants, dry cleaning, etc.). Many variations are possible.

The ranking module 306 can rank groups of content recommendations for users. For example, an applicable scenario may be associated with multiple groups of content recommendations. For example, the scenario may be associated with a group of page recommendations, a group of social group recommendations, and a group of job recommendations. In this example, the ranking module 306 can rank the multiple groups of content recommendations based on features corresponding to a given user. In some embodiments, a ranking order for the multiple groups of content recommendations can be determined based on a machine learning model. For example, the machine learning model can be trained to predict types of content that are preferred by a given user based on features associated with that user. In some embodiments, the machine learning model can be trained based on a set of training examples. For example, each training example can include a set of features corresponding to a user and supervisory signals indicating types of content the user tends to prefer when interacting with the content provider (e.g., pages, groups, events, offers, jobs, photos, videos, or posts). The features corresponding to the user can be determined by the feature module 204, as described above.

FIG. 4 illustrates an example diagram 400 of an interface 406 supported by the scenario-based recommendation module 102, according to an embodiment of the present technology. The interface 406 may be provided through a display screen 404 of a computing device 402 of a user by a software application (e.g., a social networking application) running on the computing device 402. In the example of FIG. 4, the interface 406 is shown providing groups of content recommendations associated with a “weekend planning” scenario 408. The “weekend planning” scenario 408 can be determined by a machine learning model that is trained to predict scenarios that apply to users, as described in more detail herein. In this example, a set of features corresponding to the user can be provided to the machine learning model to determine that the scenario is applicable to the user. The interface 406 is shown providing a group of event recommendations 410 and a group of offer recommendations 414 related to the scenario. In this example, the group of event recommendations 410 and the group of offer recommendations 414 are presented to the user based on their association with the “weekend planning” scenario 408 and their selection after ranking by a machine learning model of some or all groups of content recommendations associated with the scenario. Each group of recommendations can be composed of a number of related content items that can be accessed through a content provider (e.g., the social networking system 630 of FIG. 6). For example, in FIG. 4, the group of event recommendations 410 includes a set of content items 412 referencing events that are occurring over the weekend. A user operating the computing device 402 can access any of the recommended content through the content provider. Further, the interface 406 can be updated with new groups of content recommendations as other scenarios are determined to apply to the user. Many variations are possible.

FIG. 5 illustrates an example method 500, according to an embodiment of the present technology. It should be appreciated that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments discussed herein unless otherwise stated.

At block 502, at least one scenario that applies to a user of a social networking system can be determined based at least in part on features associated with the user. At block 504, one or more groups of content recommendations associated with the at least one scenario can be determined. Each group of content recommendations can include a set of content items that relate to the at least one scenario. At block 506, the one or more groups of content recommendations can be provided to the user as recommendations.

It is contemplated that there can be many other uses, applications, and/or variations associated with the various embodiments of the present technology. For example, in some cases, user can choose whether or not to opt-in to utilize the disclosed technology. The disclosed technology can also ensure that various privacy settings and preferences are maintained and can prevent private information from being divulged. In another example, various embodiments of the present technology can learn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that can be utilized in various scenarios, in accordance with an embodiment of the present technology. The system 600 includes one or more user devices 610, one or more external systems 620, a social networking system (or service) 630, and a network 650. In an embodiment, the social networking service, provider, and/or system discussed in connection with the embodiments described above may be implemented as the social networking system 630. For purposes of illustration, the embodiment of the system 600, shown by FIG. 6, includes a single external system 620 and a single user device 610. However, in other embodiments, the system 600 may include more user devices 610 and/or more external systems 620. In certain embodiments, the social networking system 630 is operated by a social network provider, whereas the external systems 620 are separate from the social networking system 630 in that they may be operated by different entities. In various embodiments, however, the social networking system 630 and the external systems 620 operate in conjunction to provide social networking services to users (or members) of the social networking system 630. In this sense, the social networking system 630 provides a platform or backbone, which other systems, such as external systems 620, may use to provide social networking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices (or systems) that can receive input from a user and transmit and receive data via the network 650. In one embodiment, the user device 610 is a conventional computer system executing, for example, a Microsoft Windows compatible operating system (OS), Apple OS X, and/or a Linux distribution. In another embodiment, the user device 610 can be a computing device or a device having computer functionality, such as a smart-phone, a tablet, a personal digital assistant (PDA), a mobile telephone, a laptop computer, a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.), a camera, an appliance, etc. The user device 610 is configured to communicate via the network 650. The user device 610 can execute an application, for example, a browser application that allows a user of the user device 610 to interact with the social networking system 630. In another embodiment, the user device 610 interacts with the social networking system 630 through an application programming interface (API) provided by the native operating system of the user device 610, such as iOS and ANDROID. The user device 610 is configured to communicate with the external system 620 and the social networking system 630 via the network 650, which may comprise any combination of local area and/or wide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 650 uses standard communications technologies and protocols. Thus, the network 650 can include links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriber line (DSL), etc. Similarly, the networking protocols used on the network 650 can include multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), file transfer protocol (FTP), and the like. The data exchanged over the network 650 can be represented using technologies and/or formats including hypertext markup language (HTML) and extensible markup language (XML). In addition, all or some links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), and Internet Protocol security (IPsec).

In one embodiment, the user device 610 may display content from the external system 620 and/or from the social networking system 630 by processing a markup language document 614 received from the external system 620 and from the social networking system 630 using a browser application 612. The markup language document 614 identifies content and one or more instructions describing formatting or presentation of the content. By executing the instructions included in the markup language document 614, the browser application 612 displays the identified content using the format or presentation described by the markup language document 614. For example, the markup language document 614 includes instructions for generating and displaying a web page having multiple frames that include text and/or image data retrieved from the external system 620 and the social networking system 630. In various embodiments, the markup language document 614 comprises a data file including extensible markup language (XML) data, extensible hypertext markup language (XHTML) data, or other markup language data. Additionally, the markup language document 614 may include JavaScript Object Notation (JSON) data, JSON with padding (JSONP), and JavaScript data to facilitate data-interchange between the external system 620 and the user device 610. The browser application 612 on the user device 610 may use a JavaScript compiler to decode the markup language document 614.

The markup language document 614 may also include, or link to, applications or application frameworks such as FLASH™ or Unity™ applications, the Silverlight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies 616 including data indicating whether a user of the user device 610 is logged into the social networking system 630, which may enable modification of the data communicated from the social networking system 630 to the user device 610.

The external system 620 includes one or more web servers that include one or more web pages 622 a, 622 b, which are communicated to the user device 610 using the network 650. The external system 620 is separate from the social networking system 630. For example, the external system 620 is associated with a first domain, while the social networking system 630 is associated with a separate social networking domain. Web pages 622 a, 622 b, included in the external system 620, comprise markup language documents 614 identifying content and including instructions specifying formatting or presentation of the identified content. As discussed previously, it should be appreciated that there can be many variations or other possibilities.

The social networking system 630 includes one or more computing devices for a social network, including a plurality of users, and providing users of the social network with the ability to communicate and interact with other users of the social network. In some instances, the social network can be represented by a graph, i.e., a data structure including edges and nodes. Other data structures can also be used to represent the social network, including but not limited to databases, objects, classes, meta elements, files, or any other data structure. The social networking system 630 may be administered, managed, or controlled by an operator. The operator of the social networking system 630 may be a human being, an automated application, or a series of applications for managing content, regulating policies, and collecting usage metrics within the social networking system 630. Any type of operator may be used.

Users may join the social networking system 630 and then add connections to any number of other users of the social networking system 630 to whom they desire to be connected. As used herein, the term “friend” refers to any other user of the social networking system 630 to whom a user has formed a connection, association, or relationship via the social networking system 630. For example, in an embodiment, if users in the social networking system 630 are represented as nodes in the social graph, the term “friend” can refer to an edge formed between and directly connecting two user nodes.

Connections may be added explicitly by a user or may be automatically created by the social networking system 630 based on common characteristics of the users (e.g., users who are alumni of the same educational institution). For example, a first user specifically selects a particular other user to be a friend. Connections in the social networking system 630 are usually in both directions, but need not be, so the terms “user” and “friend” depend on the frame of reference. Connections between users of the social networking system 630 are usually bilateral (“two-way”), or “mutual,” but connections may also be unilateral, or “one-way.” For example, if Bob and Joe are both users of the social networking system 630 and connected to each other, Bob and Joe are each other's connections. If, on the other hand, Bob wishes to connect to Joe to view data communicated to the social networking system 630 by Joe, but Joe does not wish to form a mutual connection, a unilateral connection may be established. The connection between users may be a direct connection; however, some embodiments of the social networking system 630 allow the connection to be indirect via one or more levels of connections or degrees of separation.

In addition to establishing and maintaining connections between users and allowing interactions between users, the social networking system 630 provides users with the ability to take actions on various types of items supported by the social networking system 630. These items may include groups or networks (i.e., social networks of people, entities, and concepts) to which users of the social networking system 630 may belong, events or calendar entries in which a user might be interested, computer-based applications that a user may use via the social networking system 630, transactions that allow users to buy or sell items via services provided by or through the social networking system 630, and interactions with advertisements that a user may perform on or off the social networking system 630. These are just a few examples of the items upon which a user may act on the social networking system 630, and many others are possible. A user may interact with anything that is capable of being represented in the social networking system 630 or in the external system 620, separate from the social networking system 630, or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety of entities. For example, the social networking system 630 enables users to interact with each other as well as external systems 620 or other entities through an API, a web service, or other communication channels. The social networking system 630 generates and maintains the “social graph” comprising a plurality of nodes interconnected by a plurality of edges. Each node in the social graph may represent an entity that can act on another node and/or that can be acted on by another node. The social graph may include various types of nodes. Examples of types of nodes include users, non-person entities, content items, web pages, groups, activities, messages, concepts, and any other things that can be represented by an object in the social networking system 630. An edge between two nodes in the social graph may represent a particular kind of connection, or association, between the two nodes, which may result from node relationships or from an action that was performed by one of the nodes on the other node. In some cases, the edges between nodes can be weighted. The weight of an edge can represent an attribute associated with the edge, such as a strength of the connection or association between nodes. Different types of edges can be provided with different weights. For example, an edge created when one user “likes” another user may be given one weight, while an edge created when a user befriends another user may be given a different weight.

As an example, when a first user identifies a second user as a friend, an edge in the social graph is generated connecting a node representing the first user and a second node representing the second user. As various nodes relate or interact with each other, the social networking system 630 modifies edges connecting the various nodes to reflect the relationships and interactions.

The social networking system 630 also includes user-generated content, which enhances a user's interactions with the social networking system 630. User-generated content may include anything a user can add, upload, send, or “post” to the social networking system 630. For example, a user communicates posts to the social networking system 630 from a user device 610. Posts may include data such as status updates or other textual data, location information, images such as photos, videos, links, music or other similar data and/or media. Content may also be added to the social networking system 630 by a third party. Content “items” are represented as objects in the social networking system 630. In this way, users of the social networking system 630 are encouraged to communicate with each other by posting text and content items of various types of media through various communication channels. Such communication increases the interaction of users with each other and increases the frequency with which users interact with the social networking system 630.

The social networking system 630 includes a web server 632, an API request server 634, a user profile store 636, a connection store 638, an action logger 640, an activity log 642, and an authorization server 644. In an embodiment of the invention, the social networking system 630 may include additional, fewer, or different components for various applications. Other components, such as network interfaces, security mechanisms, load balancers, failover servers, management and network operations consoles, and the like are not shown so as to not obscure the details of the system.

The user profile store 636 maintains information about user accounts, including biographic, demographic, and other types of descriptive information, such as work experience, educational history, hobbies or preferences, location, and the like that has been declared by users or inferred by the social networking system 630. This information is stored in the user profile store 636 such that each user is uniquely identified. The social networking system 630 also stores data describing one or more connections between different users in the connection store 638. The connection information may indicate users who have similar or common work experience, group memberships, hobbies, or educational history. Additionally, the social networking system 630 includes user-defined connections between different users, allowing users to specify their relationships with other users. For example, user-defined connections allow users to generate relationships with other users that parallel the users' real-life relationships, such as friends, co-workers, partners, and so forth. Users may select from predefined types of connections, or define their own connection types as needed. Connections with other nodes in the social networking system 630, such as non-person entities, buckets, cluster centers, images, interests, pages, external systems, concepts, and the like are also stored in the connection store 638.

The social networking system 630 maintains data about objects with which a user may interact. To maintain this data, the user profile store 636 and the connection store 638 store instances of the corresponding type of objects maintained by the social networking system 630. Each object type has information fields that are suitable for storing information appropriate to the type of object. For example, the user profile store 636 contains data structures with fields suitable for describing a user's account and information related to a user's account. When a new object of a particular type is created, the social networking system 630 initializes a new data structure of the corresponding type, assigns a unique object identifier to it, and begins to add data to the object as needed. This might occur, for example, when a user becomes a user of the social networking system 630, the social networking system 630 generates a new instance of a user profile in the user profile store 636, assigns a unique identifier to the user account, and begins to populate the fields of the user account with information provided by the user.

The connection store 638 includes data structures suitable for describing a user's connections to other users, connections to external systems 620 or connections to other entities. The connection store 638 may also associate a connection type with a user's connections, which may be used in conjunction with the user's privacy setting to regulate access to information about the user. In an embodiment of the invention, the user profile store 636 and the connection store 638 may be implemented as a federated database.

Data stored in the connection store 638, the user profile store 636, and the activity log 642 enables the social networking system 630 to generate the social graph that uses nodes to identify various objects and edges connecting nodes to identify relationships between different objects. For example, if a first user establishes a connection with a second user in the social networking system 630, user accounts of the first user and the second user from the user profile store 636 may act as nodes in the social graph. The connection between the first user and the second user stored by the connection store 638 is an edge between the nodes associated with the first user and the second user. Continuing this example, the second user may then send the first user a message within the social networking system 630. The action of sending the message, which may be stored, is another edge between the two nodes in the social graph representing the first user and the second user. Additionally, the message itself may be identified and included in the social graph as another node connected to the nodes representing the first user and the second user.

In another example, a first user may tag a second user in an image that is maintained by the social networking system 630 (or, alternatively, in an image maintained by another system outside of the social networking system 630). The image may itself be represented as a node in the social networking system 630. This tagging action may create edges between the first user and the second user as well as create an edge between each of the users and the image, which is also a node in the social graph. In yet another example, if a user confirms attending an event, the user and the event are nodes obtained from the user profile store 636, where the attendance of the event is an edge between the nodes that may be retrieved from the activity log 642. By generating and maintaining the social graph, the social networking system 630 includes data describing many different types of objects and the interactions and connections among those objects, providing a rich source of socially relevant information.

The web server 632 links the social networking system 630 to one or more user devices 610 and/or one or more external systems 620 via the network 650. The web server 632 serves web pages, as well as other web-related content, such as Java, JavaScript, Flash, XML, and so forth. The web server 632 may include a mail server or other messaging functionality for receiving and routing messages between the social networking system 630 and one or more user devices 610. The messages can be instant messages, queued messages (e.g., email), text and SMS messages, or any other suitable messaging format.

The API request server 634 allows one or more external systems 620 and user devices 610 to call access information from the social networking system 630 by calling one or more API functions. The API request server 634 may also allow external systems 620 to send information to the social networking system 630 by calling APIs. The external system 620, in one embodiment, sends an API request to the social networking system 630 via the network 650, and the API request server 634 receives the API request. The API request server 634 processes the request by calling an API associated with the API request to generate an appropriate response, which the API request server 634 communicates to the external system 620 via the network 650. For example, responsive to an API request, the API request server 634 collects data associated with a user, such as the user's connections that have logged into the external system 620, and communicates the collected data to the external system 620. In another embodiment, the user device 610 communicates with the social networking system 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from the web server 632 about user actions on and/or off the social networking system 630. The action logger 640 populates the activity log 642 with information about user actions, enabling the social networking system 630 to discover various actions taken by its users within the social networking system 630 and outside of the social networking system 630. Any action that a particular user takes with respect to another node on the social networking system 630 may be associated with each user's account, through information maintained in the activity log 642 or in a similar database or other data repository. Examples of actions taken by a user within the social networking system 630 that are identified and stored may include, for example, adding a connection to another user, sending a message to another user, reading a message from another user, viewing content associated with another user, attending an event posted by another user, posting an image, attempting to post an image, or other actions interacting with another user or another object. When a user takes an action within the social networking system 630, the action is recorded in the activity log 642. In one embodiment, the social networking system 630 maintains the activity log 642 as a database of entries. When an action is taken within the social networking system 630, an entry for the action is added to the activity log 642. The activity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actions that occur within an entity outside of the social networking system 630, such as an external system 620 that is separate from the social networking system 630. For example, the action logger 640 may receive data describing a user's interaction with an external system 620 from the web server 632. In this example, the external system 620 reports a user's interaction according to structured actions and objects in the social graph.

Other examples of actions where a user interacts with an external system 620 include a user expressing an interest in an external system 620 or another entity, a user posting a comment to the social networking system 630 that discusses an external system 620 or a web page 622 a within the external system 620, a user posting to the social networking system 630 a Uniform Resource Locator (URL) or other identifier associated with an external system 620, a user attending an event associated with an external system 620, or any other action by a user that is related to an external system 620. Thus, the activity log 642 may include actions describing interactions between a user of the social networking system 630 and an external system 620 that is separate from the social networking system 630.

The authorization server 644 enforces one or more privacy settings of the users of the social networking system 630. A privacy setting of a user determines how particular information associated with a user can be shared. The privacy setting comprises the specification of particular information associated with a user and the specification of the entity or entities with whom the information can be shared. Examples of entities with which information can be shared may include other users, applications, external systems 620, or any entity that can potentially access the information. The information that can be shared by a user comprises user account information, such as profile photos, phone numbers associated with the user, user's connections, actions taken by the user such as adding a connection, changing user profile information, and the like.

The privacy setting specification may be provided at different levels of granularity. For example, the privacy setting may identify specific information to be shared with other users; the privacy setting identifies a work phone number or a specific set of related information, such as, personal information including profile photo, home phone number, and status. Alternatively, the privacy setting may apply to all the information associated with the user. The specification of the set of entities that can access particular information can also be specified at various levels of granularity. Various sets of entities with which information can be shared may include, for example, all friends of the user, all friends of friends, all applications, or all external systems 620. One embodiment allows the specification of the set of entities to comprise an enumeration of entities. For example, the user may provide a list of external systems 620 that are allowed to access certain information. Another embodiment allows the specification to comprise a set of entities along with exceptions that are not allowed to access the information. For example, a user may allow all external systems 620 to access the user's work information, but specify a list of external systems 620 that are not allowed to access the work information. Certain embodiments call the list of exceptions that are not allowed to access certain information a “block list”. External systems 620 belonging to a block list specified by a user are blocked from accessing the information specified in the privacy setting. Various combinations of granularity of specification of information, and granularity of specification of entities, with which information is shared are possible. For example, all personal information may be shared with friends whereas all work information may be shared with friends of friends.

The authorization server 644 contains logic to determine if certain information associated with a user can be accessed by a user's friends, external systems 620, and/or other applications and entities. The external system 620 may need authorization from the authorization server 644 to access the user's more private and sensitive information, such as the user's work phone number. Based on the user's privacy settings, the authorization server 644 determines if another user, the external system 620, an application, or another entity is allowed to access information associated with the user, including information about actions taken by the user.

In some embodiments, the social networking system 630 can include a scenario-based recommendation module 646. The scenario-based recommendation module 646, for example, can be implemented as some or all of the functionality of the scenario-based recommendation module 102 of FIG. 1. In some embodiments, some or all functionality of the scenario-based recommendation module 646 can be implemented in the user device 610. There can be many variations.

Hardware Implementation

The foregoing processes and features can be implemented by a wide variety of machine and computer system architectures and in a wide variety of network and computing environments. FIG. 7 illustrates an example of a computer system 700 that may be used to implement one or more of the embodiments described herein in accordance with an embodiment of the invention. The computer system 700 includes sets of instructions for causing the computer system 700 to perform the processes and features discussed herein. The computer system 700 may be connected (e.g., networked) to other machines. In a networked deployment, the computer system 700 may operate in the capacity of a server machine or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. In an embodiment of the invention, the computer system 700 may be the social networking system 630, the user device 610, and the external system 720, or a component thereof. In an embodiment of the invention, the computer system 700 may be one server among many that constitutes all or part of the social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and one or more executable modules and drivers, stored on a computer-readable medium, directed to the processes and features described herein. Additionally, the computer system 700 includes a high performance input/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710 couples processor 702 to high performance I/O bus 706, whereas I/O bus bridge 712 couples the two buses 706 and 708 to each other. A system memory 714 and one or more network interfaces 716 couple to high performance I/O bus 706. The computer system 700 may further include video memory and a display device coupled to the video memory (not shown). Mass storage 718 and I/O ports 720 couple to the standard I/O bus 708. The computer system 700 may optionally include a keyboard and pointing device, a display device, or other input/output devices (not shown) coupled to the standard I/O bus 708. Collectively, these elements are intended to represent a broad category of computer hardware systems, including but not limited to computer systems based on the x86-compatible processors manufactured by Intel Corporation of Santa Clara, Calif., and the x86-compatible processors manufactured by Advanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as any other suitable processor.

An operating system manages and controls the operation of the computer system 700, including the input and output of data to and from software applications (not shown). The operating system provides an interface between the software applications being executed on the system and the hardware components of the system. Any suitable operating system may be used, such as the LINUX Operating System, the Apple Macintosh Operating System, available from Apple Computer Inc. of Cupertino, Calif., UNIX operating systems, Microsoft® Windows® operating systems, BSD operating systems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detail below. In particular, the network interface 716 provides communication between the computer system 700 and any of a wide range of networks, such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. The mass storage 718 provides permanent storage for the data and programming instructions to perform the above-described processes and features implemented by the respective computing systems identified above, whereas the system memory 714 (e.g., DRAM) provides temporary storage for the data and programming instructions when executed by the processor 702. The I/O ports 720 may be one or more serial and/or parallel communication ports that provide communication between additional peripheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures, and various components of the computer system 700 may be rearranged. For example, the cache 704 may be on-chip with processor 702. Alternatively, the cache 704 and the processor 702 may be packed together as a “processor module”, with processor 702 being referred to as the “processor core”. Furthermore, certain embodiments of the invention may neither require nor include all of the above components. For example, peripheral devices coupled to the standard I/O bus 708 may couple to the high performance I/O bus 706. In addition, in some embodiments, only a single bus may exist, with the components of the computer system 700 being coupled to the single bus. Moreover, the computer system 700 may include additional components, such as additional processors, storage devices, or memories.

In general, the processes and features described herein may be implemented as part of an operating system or a specific application, component, program, object, module, or series of instructions referred to as “programs”. For example, one or more programs may be used to execute specific processes described herein. The programs typically comprise one or more instructions in various memory and storage devices in the computer system 700 that, when read and executed by one or more processors, cause the computer system 700 to perform operations to execute the processes and features described herein. The processes and features described herein may be implemented in software, firmware, hardware (e.g., an application specific integrated circuit), or any combination thereof.

In one implementation, the processes and features described herein are implemented as a series of executable modules run by the computer system 700, individually or collectively in a distributed computing environment. The foregoing modules may be realized by hardware, executable modules stored on a computer-readable medium (or machine-readable medium), or a combination of both. For example, the modules may comprise a plurality or series of instructions to be executed by a processor in a hardware system, such as the processor 702. Initially, the series of instructions may be stored on a storage device, such as the mass storage 718. However, the series of instructions can be stored on any suitable computer readable storage medium. Furthermore, the series of instructions need not be stored locally, and could be received from a remote storage device, such as a server on a network, via the network interface 716. The instructions are copied from the storage device, such as the mass storage 718, into the system memory 714 and then accessed and executed by the processor 702. In various implementations, a module or modules can be executed by a processor or multiple processors in one or multiple locations, such as multiple servers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to, recordable type media such as volatile and non-volatile memory devices; solid state memories; floppy and other removable disks; hard disk drives; magnetic media; optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks (DVDs)); other similar non-transitory (or transitory), tangible (or non-tangible) storage medium; or any type of medium suitable for storing, encoding, or carrying a series of instructions for execution by the computer system 700 to perform any one or more of the processes and features described herein.

For purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the description. It will be apparent, however, to one skilled in the art that embodiments of the disclosure can be practiced without these specific details. In some instances, modules, structures, processes, features, and devices are shown in block diagram form in order to avoid obscuring the description. In other instances, functional block diagrams and flow diagrams are shown to represent data and logic flows. The components of block diagrams and flow diagrams (e.g., modules, blocks, structures, devices, features, etc.) may be variously combined, separated, removed, reordered, and replaced in a manner other than as expressly described and depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”, “other embodiments”, “one series of embodiments”, “some embodiments”, “various embodiments”, or the like means that a particular feature, design, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of, for example, the phrase “in one embodiment” or “in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, whether or not there is express reference to an “embodiment” or the like, various features are described, which may be variously combined and included in some embodiments, but also variously omitted in other embodiments. Similarly, various features are described that may be preferences or requirements for some embodiments, but not other embodiments.

The language used herein has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. 

What is claimed is:
 1. A computer-implemented method comprising: determining, by a computing system, at least one scenario that applies to a user of a system based on a machine learning model that processes features associated with the user; determining, by the computing system, at least a first group of content recommendations and a second group of content recommendations associated with the at least one scenario, wherein a group of content recommendations includes a set of content items that relate to the at least one scenario; and providing, by the computing system, at least the first group of content recommendations and the second group of content recommendations to the user as recommendations through the system, wherein content items included in the first group of content recommendations correspond to a first type, and wherein content items included in the second group of content recommendations correspond to a second type.
 2. The computer-implemented method of claim 1, wherein determining the at least one scenario that applies to the user further comprises: determining, by the computing system, the features associated with the user; providing, by the computing system, information describing the features to the machine learning model; and obtaining, by the computing system, information indicating the at least one scenario applies to the user from the machine learning model.
 3. The computer-implemented method of claim 2, wherein the features associated with the user include at least one of: age, gender, education level, marital status, number of children, location of residence, preferred language, topics of interest, or temporal information.
 4. The computer-implemented method of claim 1, wherein content items that correspond to the first type are different from content items that correspond to the second type.
 5. The computer-implemented method of claim 4, wherein the content items included in the first group of content recommendations are pages, social groups, events, offers, jobs, photos, videos, or posts that are accessible through the system.
 6. The computer-implemented method of claim 1, wherein determining at least the first group of content recommendations associated with the at least one scenario further comprises: determining, by the computing system, a set of content items of a same type; and determining, by the computing system, a plurality of content items that relate to the at least one scenario from the set of content items.
 7. The computer-implemented method of claim 6, wherein content items are determined to relate to the at least one scenario based at least in part on respective topics associated with the content items and subject matter reflected in the content items.
 8. The computer-implemented method of claim 1, further comprising: obtaining, by the computing system, a ranking order for the first group of content recommendations and the second group of content recommendations based at least in part on the features associated with the user.
 9. The computer-implemented method of claim 1, wherein the the first group of content recommendations and the second group of content recommendations are provided in a content feed accessible to the user through the system.
 10. The computer-implemented method of claim 1, wherein the the first group of content recommendations and the second group of content recommendations are provided as notifications sent through the system.
 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: determining at least one scenario that applies to a user of a system based on a machine learning model that processes features associated with the user; determining at least a first group of content recommendations and a second group of content recommendations associated with the at least one scenario, wherein a group of content recommendations includes a set of content items that relate to the at least one scenario; and providing at least the first group of content recommendations and the second group of content recommendations to the user as recommendations through the system, wherein content items included in the first group of content recommendations correspond to a first type, and wherein content items included in the second group of content recommendations correspond to a second type.
 12. The system of claim 11, wherein determining the at least one scenario that applies to the user further causes the system to perform: determining the features associated with the user; providing information describing the features to the machine learning model; and obtaining information indicating the at least one scenario applies to the user from the machine learning model.
 13. The system of claim 12, wherein the features associated with the user include at least one of: age, gender, education level, marital status, number of children, location of residence, preferred language, topics of interest, or temporal information.
 14. The system of claim 11, wherein content items that correspond to the first type are different from content items that correspond to the second type.
 15. The system of claim 14, wherein the content items included in the first group of content recommendations are pages, social groups, events, offers, jobs, photos, videos, or posts that are accessible through the system.
 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: determining at least one scenario that applies to a user of a system based on a machine learning model that processes features associated with the user; determining at least a first group of content recommendations and a second group of content recommendations associated with the at least one scenario, wherein a group of content recommendations includes a set of content items that relate to the at least one scenario; and providing at least the first group of content recommendations and the second group of content recommendations to the user as recommendations through the system, wherein content items included in the first group of content recommendations correspond to a first type, and wherein content items included in the second group of content recommendations correspond to a second type.
 17. The non-transitory computer-readable storage medium of claim 16, wherein determining the at least one scenario that applies to the user further causes the system to perform: determining the features associated with the user; providing information describing the features to the machine learning model; and obtaining information indicating the at least one scenario applies to the user from the machine learning model.
 18. The non-transitory computer-readable storage medium of claim 17, wherein the features associated with the user include at least one of: age, gender, education level, marital status, number of children, location of residence, preferred language, topics of interest, or temporal information.
 19. The non-transitory computer-readable storage medium of claim 16, wherein content items that correspond to the first type are different from content items that correspond to the second type.
 20. The non-transitory computer-readable storage medium of claim 19, wherein the content items included in the first group of content recommendations are pages, social groups, events, offers, jobs, photos, videos, or posts that are accessible through the system. 